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Int. J. Technology Marketing, Vol. 3, No. 1, 2008 81
Copyright © 2008 Inderscience Enterprises Ltd.
Marketing research and customer analytics:
interfunctional knowledge integration
Sharman Lichtenstein*
School of Information Systems
Deakin University
221 Burwood Highway
Burwood 3125, Australia
Fax: +61 3 92446928
E-mail: sharman.lichtenstein@deakin.edu.au
*Corresponding author
David H.B. Bednall and Stewart Adam
Bowater School of Management and Marketing
Deakin University
221 Burwood Highway
Burwood 3125, Australia
E-mail: david.bednall@deakin.edu.au
E-mail: stewart.adam@deakin.edu.au
Abstract: The Customer Analytics (CA) function is increasingly leveraged for
Customer Relationship Management (CRM), however it may lack the value of
marketing knowledge available from the Marketing Research (MR) function
due to inadequate interfunctional knowledge integration. This paper develops a
set of sixteen propositions from a synthesis of the marketing and knowledge
management literatures relating key organisational influences on the integration
of knowledge between the MR and CA functions. A range of strategic, cultural,
structural and technical influences is reflected by the propositions. It is planned
to test the propositions in future empirical research.
Keywords: Customer Analytics; CA; Marketing Research; MR; Customer
Relationship Management; CRM; market intelligence; data warehouse;
integrated marketing knowledge; technology marketing.
Reference to this paper should be made as follows: Lichtenstein, S., Bednall,
D.H.B. and Adam, S. (2008) ‘Marketing research and customer analytics:
interfunctional knowledge integration’, Int. J. Technology Marketing, Vol. 3,
No. 1, pp.81–96.
Biographical notes: Dr. Sharman Lichtenstein is an Associate Professor within
the School of Information Systems at Deakin University in Australia. Her
research has focused on internet security and privacy, organisational knowledge
management, and web-based self-service. She has developed foundational
guidelines for internet security policy and internet use policy.
82 S. Lichtenstein, D.H.B. Bednall and S. Adam
Dr. David H.B. Bednall is an Associate Professor within the Bowater School of
Management and Marketing at Deakin University. His research interests
concern the market research industry and its practices, especially how its use
and effectiveness is affected by corporate strategy and resources. Currently he
is a judge of the research industry’s Market Research Effectiveness Awards.
Dr. Stewart Adam is an Associate Professor in the Bowater School of
Management and Marketing at Deakin University in Australia. His research
interests include direct and digital marketing, internet survey response
techniques, knowledge utilisation, market orientation, and the use of
marketing and financial performance measures. He has developed the
Marketing Readiness of Website Indicator (MRWI) which is used to audit
website utilisation.
1 Introduction
A positive outcome of a Customer Relationship Management (CRM) system is the
generation of massive quantities of Customer Analytics (CA) information. But such an
outcome also creates a problem – namely how to combine this information with other
sources of marketing data, especially Marketing Research (MR). This problem provides
the focus of this paper. We argue that the integration of these marketing information
sources is necessary for a market-oriented organisation to seize opportunities and to limit
duplication and waste.
Successful market orientation is often depicted as relying on the superior integration
and utilisation of marketing intelligence sources (Guenzi and Troilo, 2006; Maltz and
Kohli, 2000; Menon and Varadarajan, 1992). In support of this decade-old claim, recent
research on CRM advocates the value of a cross-functional integrative approach to CRM
where relevant knowledge from diverse sources is coordinated and integrated (Payne and
Frow, 2006). Recent thinking suggests that successful CRM is built upon pre-existing
organisational capabilities including:
• the ability to integrate information and knowledge across functions (Bolton and
Tarasi, 2006; Plakoyiannaki and Tzokas, 2002)
• a good fit between CRM strategy and marketing strategy (Bohling et al., 2006)
• high levels of intra-organisational and inter-organisational cooperation and
coordination between involved entities (Bohling et al., 2006).
In sum, information and knowledge integration across functions is recognised by
marketers as foundational to successful CRM and deserving of greater research attention
(Bohling et al., 2006).
However, marketing research knowledge is notoriously underutilised by other
marketing functions when making strategic marketing decisions (Deshpandé and
Zaltman, 1982; Dolnicar and Schoesser, 2003; Luck and Krum, 1981) presenting an
important problem that should be addressed by scholars and practitioners. It has been
claimed that the most common influences on the under-utilisation of marketing
knowledge are organisational (Menon and Varadarajan, 1992). Consequently this paper
explores, through a synthesis of relevant literature, potential organisational influences on
the integration of MR knowledge with knowledge from the CA function. This research
MR and CA: interfunctional knowledge integration 8
3
question is important to address for four main reasons. Firstly, business analytics (of
which analysis of customer data is a part) are critical for obtaining a competitive
advantage and should be carefully managed (Davenport, 2006). Secondly, as mentioned,
market research intelligence is too often ignored by marketing managers in making
marketing decisions and other marketing analyses (Deshpandé and Zaltman, 1987;
Dolnicar and Schoesser, 2003). Thirdly, it is important to integrate knowledge from the
MR function with internal customer data for database marketing success (Evans et al.,
1995; Malhotra and Peterson, 2001). Fourthly, such integration is likely to reduce
marketing costs on the assumption that it avoids the duplication of effort inherent in both
MR and CA collecting information about current and potential customers. This last
justification remains untested.
The remainder of the paper proceeds as follows. The next section develops an account
of the main systems of customer information within for-profit organisations, and their
interrelationship. Section 3 reviews and synthesises relevant literature on knowledge
sharing, knowledge integration and marketing. Section 4 reviews organisational
challenges in the integration of knowledge from MR with CA, and derives a series of
propositions. Finally, a conclusion section summarises the paper and outlines ways in
which the propositions might be researched.
2 Main systems of customer information
In order to see how organisations might benefit from integrating sources of customer
information, we now review the main sources of this information. A summary list of key
terms and their definitions is presented in Table 1. The section assists in conceptualising
the relationship between MR and CA, and their wider context. It also highlights the value
of integrating customer-oriented information between MR and CA.
Table 1 Key terms and their origins
Term Definition Primary source
Business
analytics Systems designed to analyse the data contained in
the data warehouse This paper
Customer
Analytics (CA) That part of the CRM system that involves the
systematic collection, warehousing, analysis and
deployment of customer data
Following Marsella
et al. (2005)
Customer
Relationship
Management
(CRM)
CRM is a strategic approach that is
concerned with creating improved shareholder
value through the development of appropriate
relationships with key customers and
customer segments
Payne and Frow
(2004, p.168)
Data warehouse An organised repository of the codified
data, information and knowledge held by
an organisation
This paper
Database
middleware
systems
Software that assists in the interface between
multiple relational databases Haas et al. (1999)
Exographics Data items beyond a customer’s immediate
surroundings Greene and Milne (2006)
84 S. Lichtenstein, D.H.B. Bednall and S. Adam
Table 1 Key terms and their origins (continued)
Term Definition Primary source
Integrated
marketing
knowledge
The applied outcome of a contextual analysis of a
network of marketing data objects and their
related attributes
This paper
Market
Knowledge
(MK) (aka
market
information)
A broader concept than customers’ verbalised
needs and preferences in that it includes an
analysis of exogenous factors that influence
those needs and preferences. Market knowledge
includes monitoring competitor strategy
and implementation
Kohli and Jaworski
(1990, pp.4–5)
Market
orientation The generation of marketing knowledge, its
dissemination, and organisational response
to it, are seen as key aspects of an organisation’s
market orientation, which may, in
some circumstances, be a moderator of
organisational performance
Kohli and Jaworski
(1990)
A complementary, but
different, view is
proffered by Narver and
Slater (1990)
Marketing
Insights (MI) The function of collecting and analysing all
market relevant data, including the output
from CA together with MR and other
external information
This paper
Marketing
knowledge
integration
The applied outcome of a contextual analysis of a
network of marketing data objects and their
related attributes
This paper
Marketing
Research (MR) The function that links the consumer, customer,
and public to the marketer through information
–information used to identify and define
marketing opportunities and problems; generate,
refine, and evaluate marketing actions; monitor
marketing performance; and improve
understanding of marketing as a process
AMA (2007)
Touch points Situations where the customer and the
organisation interact Schultz et al. (2004)
2.1 Marketing perspective of CRM
CRM is a term that has been poorly or variously defined, depending on author focus.
From our perspective, CRM refers to a systematic approach to integrate the activities of
an organisation around the building and maintaining of customer (and other)
relationships. Using this perspective, we adopt the increasingly recognised definition of
Payne and Frow (2005):
“CRM is a strategic approach that is concerned with creating improved
shareholder value through the development of appropriate relationships with
key customers and customer segments. CRM unites the potential of relationship
marketing strategies and IT [information technology] to create profitable,
long-term relationships with customers and other key stakeholders. CRM
provides enhanced opportunities to use data and information to both understand
customers and co-create value with them. This requires a cross-functional
integration of processes, people, operations, and marketing capabilities that is
enabled through information, technology, and applications.” (p.168)
MR and CA: interfunctional knowledge integration 8
5
This definition is strongly aligned with the marketing functions of the organisation, given
that the definition emphasises long-term profitable relationships with customers. The
adoption of a CRM approach requires that various internal and external relationships are
managed (Gummesson, 1994) in a coordinated fashion. To build and manage customer
relationships, there are many touch points to consider – such as retail sales outlets, call
centres and billing (Schultz et al., 2004) – to best serve customers. Data from each
customer interaction at any touch point should be integrated with previously captured
customer-oriented information (Chan, 2005) to manage on-going contacts. Some
such interactions are managed by people – for example, sales or call centre staff – and
others by information systems such as Interactive Voice Response (IVR) systems and
self-service systems (Salomann et al., 2006).
Whenever customers make contact, customer data should be collected. The type
of data collected will vary by organisational type and customer base. In particular,
organisations where customers take out a subscription (e.g., a one-year insurance
policy) are likely to be known by name, as are customers for expensive or important
items (such as a new car) or where there is direct marketing from a catalogue. In a mass
transaction organisation, such as a supermarket, or organisations where customers are not
apt to return (e.g., a street vendor in a tourist location), more traditional sales data is
likely to be collected. Loyalty programmes involving store cards have increased the
likelihood that these transaction-based organisations collect this information and attribute
it to individuals.
Internal coordination concerning customers (such as for internal marketing,
management of front-line service employees, capture of customer information, delivery
of goods and services) is accompanied by internal interactions represented by data,
information and knowledge flows (Gebert et al., 2003). CRM systems assist with
integrating functions by coordinating such flows with data and information previously
captured. For example, if a call centre employee provides product support to a current
customer, she/he will need to know the customer’s previous contact history, especially
sales and complaints in order to effectively support the customer.
2.2 Customer analytics
The central function of a CRM system – to organise the collection and use of customer
information – is termed CA. The concept of CA is not well defined in the marketing
literature since most of it use is in the business practitioner literature (e.g., Aberdeen
Group, 2007). Thus the term CA is commonly equated only with the analysis of customer
data. Adopting a holistic definition, however, following the activities described by
Marsella et al. (2005) we define CA as, “that part of the CRM system that involves the
systematic collection, warehousing, analysis and deployment of customer data”.
The focus of CA is on customers – understanding and modelling their past behaviour
and predicting their future behaviour. CA includes elements of CRM, Business
Intelligence and Marketing Insights (MI). CA’s primary data comes from contacts with
customers. These data are stored in a Data Warehouse, which we define as, “an organized
repository of the codified data, information and knowledge held by an organization”.
Analysis of the data is performed by the business analytics function that includes the
customer analysis and modelling part of CA. Typically, complex analytical software
applies data mining and multivariate analysis, yielding potentially valuable insights into
customer behaviour. These outputs are then disseminated.
86 S. Lichtenstein, D.H.B. Bednall and S. Adam
2.3 Customer analytics within business intelligence
The CA function may be seen as merely one, albeit highly important, element of a
general stock of business intelligence functions (CIO Insights, 2005) in the firm. Business
Intelligence is more generally concerned with all strategic information relevant to an
organisation. Such information may include internal data within the CRM system
(accounting, personnel and logistics), other internal data (intellectual property and the
library function), external logistics data from the supply chain, external market data
(competitor information, exographics and geodemographics) and marketing research.
Exographics can be described as “data items beyond the spatial being of a person’s
immediate surroundings. The outer boundary of immediate surroundings is defined as
household neighbourhoods, small, geographically contiguous sets of homes” (Greene and
Milne, 2006, p.34). Examples of exographics include the climate, topography of a region
or the nearness to a large city or border. Geodemographics assign each person or
household to a small market segment, characterised by location, lifestyle and values
(Mitchell and McGoldrick, 1994).
2.4 CA and marketing research
MR is major source of information about the marketplace. MR functions to link the
marketer to customers and other stakeholders through the systematic collection of
information. Marketers use this information to identify market opportunities and to
monitor marketing performance (AMA, 2007).
Usually MR information is not collected by a CRM system unless it is linked to
surveys of existing, identified customers. However, both systems are capable of
collecting overlapping data in such areas as purchase behaviour, complaints, service
quality, demographics and lifestyle. Thus the potential for duplication is vast.
Alternatively, if the information can be effectively integrated, a richer understanding of
customers is a likely result.
MR is capable of measuring some variables that the CRM system cannot – for
example, tracking advertising exposure, examining usage in relation to attitudes, and
questioning future behavioural intentions. On the other hand, it is traditionally weaker
in accurately recording behavioural data (Cook, 1987), which is an advantage of
CRM-based systems.
Broadly speaking, MR may be split into its knowledge-enhancing and action-oriented
(decision-making) functions (Bednall and Valos, 2005). Since it also has the ability to
collect information relevant to non-customers, MR is potentially capable of tapping into
more sources of information than any CRM system. We argue that MR has a major role
in assisting the CA function at the analysis stage, as it can bring unique insights, such as
information about competitors and their customers, into the analysis.
2.5 MR, CA and the market insights functions
To reinforce an important point for our conceptualisation of the relationship between
CRM, MR and CA, MR can contribute to the Data Warehouse and hence can be used to
help analyse and model customer data. However, since MR is normally commissioned by
the marketing function, it may be disseminated directly to marketing management. There
MR and CA: interfunctional knowledge integration 8
7
it comprises a key information source for a function found in industry but not identified
in the literature so far, namely the MI function, “the collection and analysis of all market
relevant data, including the output from CA as well as marketing research, competitor,
tacit and other data”.
Other external data, such as competitor intelligence, may also contribute directly to
MI. A final component of MI is the tacit knowledge of experienced marketing experts
(Cavusgil et al., 2003) which is applied to the development of marketing strategies and
tactics. This occurs within the broader context of organisational barriers (e.g., budgets)
and facilitators (e.g., an entrepreneurial strategy).
Given that MR can be a major input into CA, but also has a separate path of
communication direct to marketers, it necessary to conceptualise how these two functions
could be organised to produce integrated marketing knowledge.
3 Knowledge sharing and knowledge integration in organisations
The previous section reviewed and conceptualised our understanding of the context
and relationship of MR and CA. It also highlighted the importance of integrating
customer-oriented knowledge between the MR and CA functions. This section reviews
knowledge integration in organisations and discusses the integration of knowledge from
MR with CA.
Firstly we define knowledge. For this paper, we have adopted a transformational
perspective of knowledge. Codified observations from a marketplace of data, when
placed in a decision context, are transformed into information (Barabba and Zaltman,
1991). In the analysis of this information, intelligence is created. When high levels of
confidence are developed in a body of intelligence, knowledge is created. Tacit
knowledge is the knowledge internalised by humans that cannot be shared (Polanyi,
1997) while explicit knowledge can be articulated (Nonaka and Takeuchi, 1995).
The sharing of knowledge is an important aspect of knowledge integration. Broadly,
there are three main approaches to knowledge sharing (Hansen et al., 1999; Wenger
et al., 2002). First, knowledge can be articulated, codified and stored in repositories (or
data warehouses as we term them) for later retrieval and application. Formal knowledge
is typically shared this way. Second, knowledge sharing can take place during
interpersonal communication leading to meaning making and learning. Knowledge
exchange approaches using technologies such as e-mail or the creation of virtual
communities enable communication and collaboration. Informal knowledge is typically
shared this way. Third, community-based sharing may lead to shared understandings that
are useful for knowledge integration (Wenger et al., 2002). Web-based technologies,
especially intranets, are popular supporting mechanisms for such communities.
The process of interest in this paper is knowledge integration, which relies on
strategies of knowledge sharing (Grant, 1996). Knowledge integration has been defined
as the synthesis of knowledge into situation-specific systemic knowledge for the
purposes of application (Alavi and Tiwana, 2002). Shared knowledge is combined using
various integrative mechanisms such as rules, coordinative routines, virtual teams,
cross-functional projects, and communities of practice (Alavi and Tiwana, 2002; Grant,
1996; Huang and Newell, 2003). Knowledge integration by collective human activity is
often linked to decision-making processes. Knowledge must be assembled from different
88 S. Lichtenstein, D.H.B. Bednall and S. Adam
human sources to solve problems and make decisions because the likelihood that one
person will contain all the relevant knowledge is small given organisational structures
centred on specialisation and the limitations (bounded rationality) of the human mind
(Jensen and Meckling, 1992). The ultimate aim in fostering such integration is to improve
business performance. Empirical research has shown an enhanced association between
sharing customer information across the organisation and business performance when a
CRM system enables such sharing (Jayachandran et al., 2005).
The concept of integrated marketing knowledge in firms requires re-definition. A
decade ago, market knowledge integration was understood as a marketer using marketing
to improve his/her market understanding or to make or implement a marketing decision
(Maltz and Kohli, 1996). However there is a need for a more technically based definition
to enable better insights regarding the integration of MR and CA knowledge. We
therefore present our understandings of marketing knowledge integration next.
3.1 Integrating knowledge from marketing research with customer analytics
CA is enabled by a knowledge management system centred on a data warehouse and a
business analytics function comprised of marketing data objects and associated attributes,
among other information. An example of a data object is a household, which may be
associated with attributes such as size, income and social class. Ideally, all data related to
a marketing object is stored in a data warehouse and conceptually related to that object.
As some data is qualitative (e.g., a salesperson’s reports on a competitor), integrating
heterogeneous data in a decision context is no simple matter. Database middleware
systems can assist in integrating data from multiple sources (Haas et al., 1999).
Regardless, the eventual knowledge management system is intended to yield useful
market knowledge. Data in the warehouse should therefore be linked on many levels; in
this sense the system should resemble a semantic network (Huang et al., 2007). For
example, a competitor data object may be conceptually related both to (1) information
about customer use of competitor products and (2) information about competitor product
range. Knowledge developed from the knowledge management system (based on the data
warehouse) is based on an interpretation and analysis of the data.
We thus define integrated marketing knowledge as, “the applied outcome of a
contextual analysis of a network of marketing data objects and their related attributes”.
As marketing knowledge management systems only capture and share explicit
knowledge, the tacit knowledge of expert marketers should also be sought. The MI
function is where expert marketers analyse, both formally (by a knowledge management
system as outlined above) and informally (for example, by face-to-face conversation),
available organised and ad hoc sources of market information. In this paper we are
interested in how marketers from MR contribute their knowledge to this process, and the
enablers of this contribution.
4 Organisational factors motivating MR and CA knowledge integration
In this section, we review organisational challenges for the integration of knowledge,
focusing on the integration of marketing knowledge. The section develops 16
propositions relating to the integration of MR knowledge with CA.
MR and CA: interfunctional knowledge integration 8
9
Many organisational factors can significantly affect the knowledge sharing and
integration processes. Organisational boundaries, decision rights, coordinating
mechanisms and the presence or lack of social networks can enable or inhibit knowledge
sharing (Kilduff and Tsai, 2003; Tsai, 2002). Reward systems and other incentives may
motivate knowledge sharing (Hall, 2001) although some research suggests otherwise
(Bock and Kim, 2002).
When two business information functions compete with one another for resources,
less marketing intelligence is shared or integrated (Cadogan et al., 2005; Maltz and
Kohli, 1996; Maltz et al., 2001). Directors of marketing research must become more
proficient at gaining resources (Adams et al., 1998), however this may lead to increased
rivalry and reduced knowledge integration. The greater the power and influence of one of
the functions over the other, the less likely personnel will be motivated to share
knowledge across functions. Equality can be partly obtained through equal remuneration
and equal promotion opportunities between employees in marketing and other functions,
leading to improved knowledge integration (Leenders and Wierenga, 2002). Hence
we propose:
P1 The greater the mismatch of resources (remuneration, promotion and
influence) between CA and MR, the greater the rivalry and ultimately the
less knowledge integration.
When marketing managers are more involved in marketing research activities (Malhotra
and Peterson, 2001) rivalry may be reduced. Such involvement may stem from
cross-functional governance of the functions. Where this occurs, resources are more
likely to be evenly distributed. Thus we propose:
P2 Where there is cross-functional governance of the two functions, resources are
more likely to be evenly distributed.
A cross-functional review board has been shown influential in integrating the marketing
function with R&D (Leenders and Wierenga, 2002). This suggests a similar relationship
between MR and CA should exist. Hence we propose:
P3 Cross-functional oversight of MR and CA positively influences the quantity
of interaction.
Such a cross-functional oversight is likely to lead to cross-functional teams and improved
integration (Leenders and Wierenga, 2002) with marketing:
P4 The presence of cross-functional teams incorporating the MR and CA units
positively influences the quantity of interaction of MR with CA.
People who work together are more likely to learn the others’ perspectives and be better
motivated to work together.
The relationships proposed here and others that follow are shown in Figure 1.
90 S. Lichtenstein, D.H.B. Bednall and S. Adam
Figure 1 Propositions developed in this paper
CA-MR
interaction
quality
CA-MR
interaction
quantity
CA-MR
proximity
CA-MR
cross-
functional
oversight
MR-CA
rivalry
CA-MR
resources
ICT
intensivity
CA-MR
information
interactional
skills
Business
strategy
Trust CA
and MR
MR-CA
cross-
functional
teams
P1
P4
P2
P3
P5 P6
P7
P8
P9
P10
P11
P12P14
P13
P15
P16
P15
Marketing
knowledge
integration and
sharing
4.1 Trust
Trust in a sharer’s knowledge appears as a key factor in marketing knowledge integration
in several studies. In one study, the frequency with which a sender and receiver from
different marketing units informally communicate appears to have no effect on the
perceived quality of the intelligence shared, however after 125 communications per
month there is increased confidence, or trust, in such intelligence (Maltz and Kohli,
1996). Interestingly, after a certain threshold the value of the additional communication
may damage trust in the sender and confidence in the quality of his/her intelligence,
although reasons for this mistrust are unclear (Maltz and Kohli, 1996). There are many
definitions of trust in both marketing and knowledge management literatures. In the
marketing literature, trust has been defined as a receiver’s perception that a sender has the
ability and motivation to provide good intelligence (Maltz and Kohli, 1996). Trust has
also been shown to influence the uptake of marketing research by other functions (Maltz
and Kohli, 1996; Moorman et al., 1993).
The role of communication and collaboration in interdepartmental knowledge
integration has been previously noted (Kahn et al., 1997). Communication in the
form of timely and honest information influences both trust and satisfaction in
business-to-business networks (Selnes, 1998). Organisational cultures of learning,
innovation, trust, collaboration and cooperation facilitate knowledge sharing while
cultures of distrust, competition and the rewarding of individual knowledge inhibit
knowledge sharing (Gold et al., 2001). We propose that:
P5 Rivalry between the two groups weakens trust.
P6 The presence of trust between personnel in MR and CA positively influences MR
and CA interaction quality.
MR and CA: interfunctional knowledge integration 91
Lack of trust is likely to lead to duplicated communication to marketing managers and
less synthesis of vital information.
4.2 Organisational structures
Cross-functional teams are likely to have people working closely together, thus
influencing trust between the parties. Hence we propose:
P7 Working in cross-functional teams improves trust.
Not only the quantity of interactions but also the perceived quality of interaction may
influence the uptake of MR by other business functions (Moorman et al., 1993). Maltz
and Kohli (1996) found that the perceived quantity of interaction between marketing
personnel influences trust which in turn influences the perceived quality of marketing
intelligence shared. Therefore we propose that:
P8 The quantity of interactions between MR and CA positively influences trust between
personnel in MR and CA up to a certain threshold.
P9 Perceived quality of interaction between personnel in each function positively
influences knowledge sharing and integration of knowledge from MR with
knowledge in CA.
These propositions assume that the more people have contact with each other, up to a
certain level, the better able they are able to work together.
4.3 Interaction skills
In additional to organisational influences on knowledge sharing there are theories which
consider individuals – sharers and receivers of knowledge – and their beliefs, attitudes
and behaviours in knowledge sharing. When there are positive relationships between
sharers and potential receivers, and a healthy level of trust, sharers are more inclined to
share knowledge (Andrews and Delahaye, 2000).
However, for receivers to access, retrieve, comprehend and assimilate a sharer’s
knowledge, sharers must not only be aware and motivated, but must share in skilled ways
that meet receiver needs (Dixon, 2002). Hendriks (2004) cautioned that “knowledge
sharing is not seen as pushing packages of existing knowledge back and forth, but as a
process that requires not only knowledge of the bringing party but also of the obtaining
party” (p.6). Thus a sharer’s perceptions of a receiver’s knowledge needs and behaviours
may influence sharer beliefs, attitudes and behaviours in knowledge sharing (Lichtenstein
and Hunter, 2006). In addition, a receiver must be able to relate incoming knowledge to
existing tacit knowledge in order to understand and assimilate it (Dixon, 2002). This can
be more difficult when sharers and believers have different perspectives or cognition
(Lane and Lubatkin, 1998). A common example is when the sharer and receiver belong to
different workgroups and experience difficulties relating to each other’s specialised
knowledge. Thus organisational structure can impact on even micro-level knowledge
sharing between individuals.
92 S. Lichtenstein, D.H.B. Bednall and S. Adam
P10 The presence of cross-functional teams involving MR and CA people influences
their interactional skills.
P11 These interaction skills influence both rivalry and trust.
These propositions suggest there is an experience curve in terms of groups working
together effectively. Cross-functional teams accelerate this learning.
4.4 Strategy as a motivator of integration
For-profit organisations vary markedly in the broad strategies they apply to maintaining
or growing their businesses. One useful typology identifies three main organisational
types (Miles and Snow, 1978). The Prospector types are dedicated to scanning both the
internal and external environments for new entrepreneurial opportunities. Defenders are
likely to operate successfully in relatively stable markets where they look for greater
efficiencies and quality to improve their prospects. The Analyser has a strategy that
combines elements of both. Prospectors are more likely to seek and use all types of MR
effectively and less likely to use it for internal political processes (Bednall and Valos,
2005). In contrast, Defenders were less likely to make effective use of MR information. It
is likely that these differences in orientation would also apply to an interest in and use of
CA, assuming it can deliver new insights or an expanded market. If even greater insights
can be gained by integrating MR and CA, it is likely that Prospectors would be more
likely and Defenders least likely to favour this. Hence, we propose:
P12 When the business strategy of an organisation is that of a Prospector, the
integration of market research with CA information is more likely to be favoured
than it is by Defenders.
P13 Prospector organisations are more likely to foster trust between the groups than
are the other strategy types.
P14 Prospector organisations are more likely to provide resources for both the MR and
CA functions than are the other strategy types.
Entrepreneurial organisations (Prospectors) depend on quality market insights and hence
have are more willing to invest in acquiring them.
4.5 Contextual factors
Maltz and Kohli (1996) noted the importance of proximity of marketing units for greater
interaction, increased trust and increased perceptions of marketing intelligence quality.
Hence we propose:
P15 The proximity of the MR and CA units positively influences the integration of
knowledge from MR with knowledge in CA and the amount of interaction between
MR and CA.
MR and CA: interfunctional knowledge integration 9
3
Recent developments in technology suggest a number of additional factors. The degree of
Information and Communication Technology (ICT) intensivity between the marketing
function and R&D has been found to correlate with the integration of the two functions
(Leenders and Wierenga, 2002). Another technological issue relates to information
system design. Separate information systems can lead to a lack of integration across
business units (Chan, 2005). Similarly, a lack of alignment of organisational processes
with CA reduces integration and organisational performance (Davenport, 2006).
Therefore we propose that:
P16 The degree of ICT intensivity positively influences the integration of MR with CA.
In practical terms, people who work nearby to one another are more likely to interact
informally, assisting the building trust and tacit knowledge. Firms who invest heavily in
ICT are more likely to value customer insights produced by using integrated marketing
information systems.
5 Conclusion
In this paper we have argued that organisations, particularly those with a Prospector
orientation, have a vested interest in integrating marketing knowledge flowing from MR
and CA. Primarily this is to leverage opportunities, but it may also help reduce waste.
The paper has highlighted the important role of MR in CRM by depicting the relationship
between CRM, CA, business intelligence and marketing insights.
As a key theoretical contribution, the paper presents a synthesis of a wide range of
representative relevant marketing literature to develop a set of sixteen key propositions
relating potential organisational influences on the integration of knowledge between the
MR function and CA. The propositions include a range of structural, cultural, technical
and strategic factors, suggesting that an organisational solution to knowledge integration
will require a multifaceted approach. The propositions also strongly suggest that a
technical solution such as a CRM system is insufficient on its own for inter-functional
knowledge integration between MR and CA.
The set of propositions developed in this paper represents a strong foundation for
empirical research. Ultimately a model like the one in Figure 1 can be tested
quantitatively, though more than one key informant per organisation is likely to be
required in order to test the comprehensive picture developed in this paper.
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